From facial recognition to autonomous vehicles, computer vision evolves every year. As part of artificial intelligence, it processes and analyzes visual data, helping us to automate tasks. But, even the most elaborate computer vision model wouldn’t work without precise data annotation.
Data annotation is the foundation of computer vision training, contributing to its success. It provides visual examples that the model recognizes and interprets. High-quality data annotation allows computer vision models to function in a wide range of scenarios, overcoming complexities of visual data. This prepares them to function effectively in diverse environments.
Let's explore the most common types and functions of computer vision and the role data annotation plays in the whole training process.
Core Functions of Computer Vision
While processing the visual data around us, computer vision performs specific tasks. Based on the initial training, computer vision can do the following:
- Image recognition. It identifies objects and people on the images and videos.
- Facial recognition. It can verify identity based on facial features.
- Object detection. It can understand the size and position of an object on the image.
- Semantic segmentation. It classifies segments of the image into categories (e.g., people, sceneries).
- Image categorization. It categorizes the entire image into classes based on its content.
To master computer vision to the fullest extent, you will need to pass through data annotation first. This computer vision service can be done by automatic tools or with help of in-house experts, such as those at Label Your Data.
Annotating Data for Computer Vision
Data annotation helps to label raw data, which is crucial for training machine learning (ML) algorithms. Annotated data acts as a guide, helping algorithms recognize patterns and characteristics that define various classes of objects, actions, or scenes. The more specific the data annotation, the better performance and reliability of computer vision applications.
For computer vision, these types of data annotation are the most popular ones:
- Object detection. This type of annotation identifies and specifies the location of objects on the frame, with usage of specific rectangular markups.
- 2D Boxes (bounding boxes). This type supposes drawing 2-dimensional frames around objects. It allows classifying the objects of interest into predefined categories.
- Polygons. Outlines the object on the image from multiple sides, giving precision on the shape of the object.
- 3D cuboids. Draws 3D boxes around objects, adding a 3D dimension. They help to identify the position of an object, showing height, width, and depth.
- LiDAR/RADAR. Also called sensor fusion, this type of annotation labels objects based on the 3D point cloud data that we receive from various sensors.
- Video annotation. Much like object detection on the image, this type of annotation does the same with the video, breaking it into smaller frames. It allows linking the object’s position over different frames.
- Optical Character Recognition (OCR). Transforms texts from the images to the regular text for further machine learning processing. Common for photocopies or scanned documents.
Best Practices of Making Data Annotation Accurate
Thanks to meticulous annotation, AI-based models can understand and interpret visual data precisely. The team of experts at Label Your Data stick to the following principles that ensure the final labeled datasets meet the initial requirements:
- Sticking to the guidelines. Before starting any annotation project, it’s important to understand the initial guidelines. They may include examples of already annotated data or description of tasks that future machine learning models will accomplish.
- Training of annotators. The annotators receive the needed training with guidelines and demo sessions. They agree on consistency and any other metrics for evaluation.
- Providing a pilot version. A small piece of the project can be done as a pilot to compare with initial guidelines and correct if needed.
- Organizing multiple quality checks. To deliver accurate annotation, QA happens multiple times during the whole project. We usually ensure quality after every task or milestone and also at the end of the project before submission.
By implementing these techniques, we can significantly improve the precision of human data annotation, thereby enhancing the performance of the machine learning models. As a result, the models will perform better, and we’ll reduce time and resources required for model training and refinement.
Role of Data Annotation in Computer Vision Performance
Data annotation provides clear and unambiguous examples of various classes and objects in visual data. With usage of accurately labeled images or videos, AI models learn more effectively. High-quality annotations often include a wide variety of examples for each class. They cover different angles, lighting conditions, and backgrounds. This diversity in the training data helps AI models to extend their learning to new, unseen images.
Comprehensive data annotation can also help mitigate biases in AI models. It ensures that the training data includes a balanced representation of various scenarios. Think of such applications as facial recognition, where this will have an impact. This type of computer vision is the most sensitive, leading to potential unfair outcomes.
Accurate annotation is a must for complex computer vision tasks that require a deep understanding of the visual scene. Some examples include segmentation or 3D reconstruction. Detailed labeling of objects, boundaries, and relationships within the image allows AI models to develop a nuanced understanding of the scene.
As a result, they make decisions with greater confidence. This makes a difference in critical applications, such as medical image analysis or autonomous driving. This confidence in the model's interpretations can lead to success or failure.
Wrapping up
The journey to mastering computer vision lies on the precision of data annotation. The meticulous labeling of images and videos provides AI systems with the clarity and specificity needed to distinguish between different objects, features, and scenarios. That’s why it is imperative for researchers and developers across various industries to recognize the critical importance of precision annotation. And to invest in the development of high-quality annotated datasets.
The more accurate the output of the computer vision model, the more elaborate solution we’ll get in the application of AI. With detailed model training and retraining, we may expect mastering computer vision to the next level.
Online Threat Alerts Security Tips
Pay the safest way
Credit cards are the safest way to pay for online purchases because you can dispute the charges if you never get the goods or services or if the offer was misrepresented. Federal law limits your liability to $50 if someone makes unauthorized charges to your account, and most credit card issuers will remove them completely if you report the problem promptly.
Guard your personal information
In any transaction you conduct, make sure to check with your state or local consumer protection agency and the Better Business Bureau (BBB) to see if the seller, charity, company, or organization is credible. Be especially wary if the entity is unfamiliar to you. Always call the number found on a website’s contact information to make sure the number legitimately belongs to the entity you are dealing with.
Be careful of the information you share
Never give out your codes, passwords or personal information, unless you are sure of who you're dealing with
Know who you’re dealing with
Crooks pretending to be from companies you do business with may call or send an email, claiming they need to verify your personal information. Don’t provide your credit card or bank account number unless you are actually paying for something and know who you are sending payment to. Your social security number should not be necessary unless you are applying for credit. Be especially suspicious if someone claiming to be from a company with whom you have an account asks for information that the business already has.
Check your accounts
Regularly check your account transactions and report any suspicious or unauthorised transactions.
Don’t believe promises of easy money
If someone claims that you can earn money with little or no work, get a loan or credit card even if you have bad credit, or make money on an investment with little or no risk, it’s probably a scam. Oftentimes, offers that seem too good to be true, actually are too good to be true.
Do not open email from people you don’t know
If you are unsure whether an email you received is legitimate, try contacting the sender directly via other means. Do not click on any links in an email unless you are sure it is safe.
Think before you click
If an email or text message looks suspicious, don’t open any attachments or click on the links.
Verify urgent requests or unsolicited emails, messages or phone calls before you respond
If you receive a message or a phone call asking for immediate action and don't know the sender, it could be a phishing message.
Be careful with links and new website addresses
Malicious website addresses may appear almost identical to legitimate sites. Scammers often use a slight variation in spelling or logo to lure you. Malicious links can also come from friends whose email has unknowingly been compromised, so be careful.
Secure your personal information
Before providing any personal information, such as your date of birth, Social Security number, account numbers, and passwords, be sure the website is secure.
Stay informed on the latest cyber threats
Keep yourself up to date on current scams by visiting this website daily.
Use Strong Passwords
Strong passwords are critical to online security.
Keep your software up to date and maintain preventative software programs
Keep all of your software applications up to date on your computers and mobile devices. Install software that provides antivirus, firewall, and email filter services.
Update the operating systems on your electronic devices
Make sure your operating systems (OSs) and applications are up to date on all of your electronic devices. Older and unpatched versions of OSs and software are the target of many hacks. Read the CISA security tip on Understanding Patches and Software Updates for more information.
What if You Got Scammed?
Stop Contact With The Scammer
Hang up the phone. Do not reply to emails, messages, or letters that the scammer sends. Do not make any more payments to the scammer. Beware of additional scammers who may contact you claiming they can help you get your lost money back.
Secure Your Finances
- Report potentially compromised bank account, credit or debit card information to your financial institution(s) immediately. They may be able to cancel or reverse fraudulent transactions.
- Notify the three major credit bureaus. They can add a fraud alert to warn potential credit grantors that you may be a victim of identity theft. You may also want to consider placing a free security freeze on your credit report. Doing so prevents lenders and others from accessing your credit report entirely, which will prevent them from extending credit:
- Equifax
- Experian
- TransUnion
Check Your Computer
If your computer was accessed or otherwise affected by a scam, check to make sure that your anti-virus is up-to-date and running and that your system is free of malware and keylogging software. You may also need to seek the help of a computer repair company. Consider utilizing the Better Business Bureau’s website to find a reputable company.
Change Your Account Passwords
Update your bank, credit card, social media, and email account passwords to try to limit further unauthorized access. Make sure to choose strong passwords when changing account passwords.
Report The Scam
Reporting helps protect others. While agencies can’t always track down perpetrators of crimes against scammers, they can utilize the information gathered to record patterns of abuse which may lead to action being taken against a company or industry.
Report your issue to the following agencies based on the nature of the scam:
- Local Law Enforcement: Consumers are encouraged to report scams to their local police department or sheriff’s office, especially if you lost money or property or had your identity compromised.
- Federal Trade Commission: Contact the Federal Trade Commission (FTC) at 1-877-FTC-HELP (1-877-382-4357) or use the Online Complaint Assistant to report various types of fraud, including counterfeit checks, lottery or sweepstakes scams, and more.
- Identitytheft.gov: If someone is using your personal information, like your Social Security, credit card, or bank account number, to open new accounts, make purchases, or get a tax refund, report it at www.identitytheft.gov. This federal government site will also help you create your Identity Theft Report and a personal recovery plan based on your situation. Questions can be directed to 877-ID THEFT.
How To Recognize a Phishing Scam
Scammers use email or text messages to try to steal your passwords, account numbers, or Social Security numbers. If they get that information, they could get access to your email, bank, or other accounts. Or they could sell your information to other scammers. Scammers launch thousands of phishing attacks like these every day — and they’re often successful.
Scammers often update their tactics to keep up with the latest news or trends, but here are some common tactics used in phishing emails or text messages:
Phishing emails and text messages often tell a story to trick you into clicking on a link or opening an attachment. You might get an unexpected email or text message that looks like it’s from a company you know or trust, like a bank or a credit card or utility company. Or maybe it’s from an online payment website or app. The message could be from a scammer, who might
- say they’ve noticed some suspicious activity or log-in attempts — they haven’t
- claim there’s a problem with your account or your payment information — there isn’t
- say you need to confirm some personal or financial information — you don’t
- include an invoice you don’t recognize — it’s fake
- want you to click on a link to make a payment — but the link has malware
- say you’re eligible to register for a government refund — it’s a scam
- offer a coupon for free stuff — it’s not real
About Online Threat Alerts (OTA)
Online Threat Alerts or OTA is an anti-cybercrime community that started in 2012. OTA alerts the public to cyber crimes and other web threats.
By alerting the public, we have prevented a lot of online users from getting scammed or becoming victims of cybercrimes.
With the ever-increasing number of people going online, it important to have a community like OTA that continuously alerts or protects those same people from cyber-criminals, scammers and hackers, who are every day finding new ways of carrying out their malicious activities.
Online users can help by reporting suspicious or malicious messages or websites to OTA. And, if they want to determine if a message or website is a threat or scam, they can use OTA's search engine to search for the website or parts of the message for information.
Help maintain Online Threat Alerts (OTA).